Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.

PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation

EarthLink: A Self-Evolving AI Agent for Climate Science

Reality Proxy: Fluid Interactions with Real-World Objects in MR via Abstract Representations

Leveraging multi-source and heterogeneous signals for fatigue detection

Segmentation-free Goodness of Pronunciation

Adaptive Relative Pose Estimation Framework with Dual Noise Tuning for Safe Approaching Maneuvers

Compositional Coordination for Multi-Robot Teams with Large Language Models

Diffusion Beats Autoregressive in Data-Constrained Settings

The New LLM Bottleneck: A Systems Perspective on Latent Attention and Mixture-of-Experts

EndoControlMag: Robust Endoscopic Vascular Motion Magnification with Periodic Reference Resetting and Hierarchical Tissue-aware Dual-Mask Control

Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation

Omni-Thinker: Scaling Cross-Domain Generalization in LLMs via Multi-Task RL with Hybrid Rewards

GCC-Spam: Spam Detection via GAN, Contrastive Learning, and Character Similarity Networks

SDSC:A Structure-Aware Metric for Semantic Signal Representation Learning

Multilingual LLMs Are Not Multilingual Thinkers: Evidence from Hindi Analogy Evaluation

Frequency-Dynamic Attention Modulation for Dense Prediction

A Survey of Deep Learning for Geometry Problem Solving

EEG Foundation Models: A Critical Review of Current Progress and Future Directions

Inversion-DPO: Precise and Efficient Post-Training for Diffusion Models

A PBN-RL-XAI Framework for Discovering a "Hit-and-Run" Therapeutic Strategy in Melanoma

Task Priors: Enhancing Model Evaluation by Considering the Entire Space of Downstream Tasks

OrQstrator: An AI-Powered Framework for Advanced Quantum Circuit Optimization

A comprehensive study of LLM-based argument classification: from LLAMA through GPT-4o to Deepseek-R1

Mechanistic Indicators of Understanding in Large Language Models

Scaling RL to Long Videos

Fast Bilateral Teleoperation and Imitation Learning Using Sensorless Force Control via Accurate Dynamics Model

Masked Autoencoders that Feel the Heart: Unveiling Simplicity Bias for ECG Analyses

SyncMapV2: Robust and Adaptive Unsupervised Segmentation

LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs

Why Do Class-Dependent Evaluation Effects Occur with Time Series Feature Attributions? A Synthetic Data Investigation

Diffuse and Disperse: Image Generation with Representation Regularization

LLM-D12: A Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Models

MambaNeXt-YOLO: A Hybrid State Space Model for Real-time Object Detection

PALADIN : Robust Neural Fingerprinting for Text-to-Image Diffusion Models

Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits

Machine Learning Solutions Integrated in an IoT Healthcare Platform for Heart Failure Risk Stratification

Beyond Low-rank Decomposition: A Shortcut Approach for Efficient On-Device Learning

Vision Transformers in Precision Agriculture: A Comprehensive Survey

PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research

LagKV: Lag-Relative Information of the KV Cache Tells Which Tokens Are Important

Trigger without Trace: Towards Stealthy Backdoor Attack on Text-to-Image Diffusion Models

Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs

Aligning Vision to Language: Annotation-Free Multimodal Knowledge Graph Construction for Enhanced LLMs Reasoning

Att-Adapter: A Robust and Precise Domain-Specific Multi-Attributes T2I Diffusion Adapter via Conditional Variational Autoencoder

When Large Vision-Language Model Meets Large Remote Sensing Imagery: Coarse-to-Fine Text-Guided Token Pruning

Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation

Tackling Hallucination from Conditional Models for Medical Image Reconstruction with DynamicDPS

Quantum Machine Learning in Precision Medicine and Drug Discovery -- A Game Changer for Tailored Treatments?

A general language model for peptide identification

ExpliCa: Evaluating Explicit Causal Reasoning in Large Language Models

EVEv2: Improved Baselines for Encoder-Free Vision-Language Models

LLM Alignment as Retriever Optimization: An Information Retrieval Perspective

Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings

Online Housing Market

Integrated Learning and Optimization for Congestion Management and Profit Maximization in Real-Time Electricity Market

Integrating Evidence into the Design of XAI and AI-based Decision Support Systems: A Means-End Framework for End-users in Construction

Scalable Parameter Design for Superconducting Quantum Circuits with Graph Neural Networks

A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospects

Neural Corrective Machine Unranking

Towards a Universal 3D Medical Multi-modality Generalization via Learning Personalized Invariant Representation

Differentiable Motion Manifold Primitives for Reactive Motion Generation under Kinodynamic Constraints

Zeroth-Order Fine-Tuning of LLMs in Random Subspaces

RUMI: Rummaging Using Mutual Information

Neural Machine Unranking

VolDoGer: LLM-assisted Datasets for Domain Generalization in Vision-Language Tasks

Unsupervised Concept Drift Detection from Deep Learning Representations in Real-time

A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models

DualXDA: Towards Sparse, Efficient and Explainable Data Attribution in Large AI Models

Quantifying the Uniqueness and Divisiveness of Presidential Discourse

DocTER: Evaluating Document-based Knowledge Editing

Learning Concepts Definable in First-Order Logic with Counting

Recognizing and Eliciting Weakly Single Crossing Profiles on Trees

Compliance Brain Assistant: Conversational Agentic AI for Assisting Compliance Tasks in Enterprise Environments

Learning Temporal Abstractions via Variational Homomorphisms in Option-Induced Abstract MDPs

When Autonomy Goes Rogue: Preparing for Risks of Multi-Agent Collusion in Social Systems

An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis

DisMS-TS: Eliminating Redundant Multi-Scale Features for Time Series Classification

Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games

Beamforming and Resource Allocation for Delay Minimization in RIS-Assisted OFDM Systems

Neurodivergent Influenceability as a Contingent Solution to the AI Alignment Problem

EducationQ: Evaluating LLMs' Teaching Capabilities Through Multi-Agent Dialogue Framework

SuperARC: An Agnostic Test for Narrow, General, and Super Intelligence Based On the Principles of Recursive Compression and Algorithmic Probability

IPCGRL: Language-Instructed Reinforcement Learning for Procedural Level Generation

OR-LLM-Agent: Automating Modeling and Solving of Operations Research Optimization Problems with Reasoning LLM

Chemical reasoning in LLMs unlocks strategy-aware synthesis planning and reaction mechanism elucidation

BEARCUBS: A benchmark for computer-using web agents

From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems

HPS: Hard Preference Sampling for Human Preference Alignment

A Differentiated Reward Method for Reinforcement Learning based Multi-Vehicle Cooperative Decision-Making Algorithms

Retrieving Classes of Causal Orders with Inconsistent Knowledge Bases

On the Structure of Game Provenance and its Applications

I-CEE: Tailoring Explanations of Image Classification Models to User Expertise

SIDA: Synthetic Image Driven Zero-shot Domain Adaptation

3D Software Synthesis Guided by Constraint-Expressive Intermediate Representation

Moving Out: Physically-grounded Human-AI Collaboration

SynC: Synthetic Image Caption Dataset Refinement with One-to-many Mapping for Zero-shot Image Captioning

Approximate SMT Counting Beyond Discrete Domains

DRWKV: Focusing on Object Edges for Low-Light Image Enhancement

BadHMP: Backdoor Attack against Human Motion Prediction

Created by

Haebom

저자

Chaohui Xu, Si Wang, Chip-Hong Chang

개요

본 논문은 하위 초 단위의 미래 인간 동작 예측에 대한 골격 기반 신경망의 취약성, 특히 회피 공격 및 백도어 공격에 대한 취약성을 다룹니다. 연구팀은 인간 동작 예측 작업을 특별히 표적으로 하는 새로운 백도어 공격인 BadHMP를 제안합니다. BadHMP는 골격의 한 부분에 국한된 백도어 트리거를 삽입하여 생성된 독성 훈련 샘플을 사용합니다. 이 트리거는 특정 관절들이 과거 시간 단계에서 미리 정의된 동작을 따르도록 유도하며, 이후 미래 시퀀스는 모든 관절이 목표 궤적을 따라 움직이도록 전반적으로 수정됩니다. 세심하게 설계된 백도어 트리거와 목표는 독성 샘플의 부드러움과 자연스러움을 보장하여 모델 트레이너가 감지하기 어렵게 하면서도 오염되지 않은 시퀀스에 대한 예측 충실도 측면에서 독성 모델이 눈에 띄지 않도록 합니다. 낮은 독성 샘플 주입 비율에서도 설계된 입력 시퀀스에 의해 목표 시퀀스를 성공적으로 활성화할 수 있습니다. Human3.6M 및 CMU-Mocap 두 개의 데이터셋과 LTD 및 HRI 두 개의 네트워크 아키텍처에 대한 실험 결과는 BadHMP의 높은 충실도, 효과 및 은밀성을 보여줍니다. 또한 미세 조정 방어에 대한 공격의 강력함도 검증되었습니다.

시사점, 한계점

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시사점:

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인간 동작 예측 모델의 백도어 취약성을 효과적으로 공격하는 새로운 방법(BadHMP)을 제시.

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BadHMP의 높은 성공률과 은밀성을 실험적으로 검증.

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미세 조정 방어에 대한 BadHMP의 강건성을 확인.

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안전에 중요한 응용 분야에서 인공지능 모델의 보안 중요성을 강조.

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한계점:

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제안된 공격이 특정 유형의 모델과 데이터셋에 국한될 가능성 존재.

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실제 세계 시나리오에서의 BadHMP의 효과에 대한 추가적인 연구 필요.

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더욱 강력한 방어 기법에 대한 연구 필요.

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다양한 백도어 트리거 및 목표에 대한 BadHMP의 일반화 성능에 대한 추가적인 분석 필요.

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